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Auckland, New Zealand

The Auckland University of Technology is a university in New Zealand. It was formed on 1 January 2000 when the Auckland Institute of Technology was granted university status. Its primary campus is on Wellesley Street in Auckland's Central business district . AUT has three secondary campuses: North Shore, South, and the Millennium Institute of Sport and Health . For branding purposes, since 2010 the Auckland University of Technology refers to itself as AUT University. Wikipedia.

Ikeda E.,Auckland University of Technology
Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation | Year: 2014

To review the use of quality of life (QOL) measures utilised in children and youth with autism spectrum disorder (ASD). Relevant articles were identified through database searches using MEDLINE, CINAHL Plus with Full Text and SPORTDiscus with Full Text via EBSCO Health Database, PsycINFO and ProQuest Health and Medicine (from 2000 to May 2013). Original research articles were included that measured QOL in children and youth with ASD aged 5-20 years. Searches were limited to articles from peer-reviewed journals, in English or German, and those available in full text. The search identified 1,165 titles and 13 met the inclusion criteria. The review identified a number of QOL measures used in children and youth with ASD, with the most common one being the Pediatric Quality of Life Inventory™ (PedsQL). QOL measures using self-reports were uncommon, and the reliability and validity of QOL measures were not sufficiently reported for this population. Large discrepancies in QOL scores were found between self-reports and proxy-reports. Despite the differences in study design and methodological quality, there was consistency in the results among studies; children and youth with ASD provided lower QOL scores, particularly for social domains, compared to their healthy counterparts. The PedsQL is likely to be an appropriate QOL measure for use in children and youth with ASD. Future research should focus on examining the appropriateness, reliability and validity of QOL self-reports for use in this population. Source

Buchheit M.,Academy for Sports Excellence | Laursen P.B.,High Performance Sport New Zealand | Laursen P.B.,Auckland University of Technology
Sports Medicine | Year: 2013

High-intensity interval training (HIT) is a well-known, time-efficient training method for improving cardiorespiratory and metabolic function and, in turn, physical performance in athletes. HIT involves repeated short (<45 s) to long (2-4 min) bouts of rather high-intensity exercise interspersed with recovery periods (refer to the previously published first part of this review). While athletes have used 'classical' HIT formats for nearly a century (e.g. repetitions of 30 s of exercise interspersed with 30 s of rest, or 2-4-min interval repetitions ran at high but still submaximal intensities), there is today a surge of research interest focused on examining the effects of short sprints and all-out efforts, both in the field and in the laboratory. Prescription of HIT consists of the manipulation of at least nine variables (e.g. work interval intensity and duration, relief interval intensity and duration, exercise modality, number of repetitions, number of series, between-series recovery duration and intensity); any of which has a likely effect on the acute physiological response. Manipulating HIT appropriately is important, not only with respect to the expected middle- to long-term physiological and performance adaptations, but also to maximize daily and/or weekly training periodization. Cardiopulmonary responses are typically the first variables to consider when programming HIT (refer to Part I). However, anaerobic glycolytic energy contribution and neuromuscular load should also be considered to maximize the training outcome. Contrasting HIT formats that elicit similar (and maximal) cardiorespiratory responses have been associated with distinctly different anaerobic energy contributions. The high locomotor speed/power requirements of HIT (i.e. ≥95 % of the minimal velocity/power that elicits maximal oxygen uptake [v/p ̇V ̇ O2max] to 100 % of maximal sprinting speed or power) and the accumulation of high-training volumes at high-exercise intensity (runners can cover up to 6-8 km at v ̇ V ̇ O2max per session) can cause significant strain on the neuromuscular/musculoskeletal system. For athletes training twice a day, and/or in team sport players training a number of metabolic and neuromuscular systems within a weekly microcycle, this added physiological strain should be considered in light of the other physical and technical/tactical sessions, so as to avoid overload and optimize adaptation (i.e. maximize a given training stimulus and minimize musculoskeletal pain and/or injury risk). In this part of the review, the different aspects of HIT programming are discussed, from work/relief interval manipulation to HIT periodization, using different examples of training cycles from different sports, with continued reference to the cardiorespiratory adaptations outlined in Part I, as well as to anaerobic glycolytic contribution and neuromuscular/musculoskeletal load. © 2013 Springer International Publishing Switzerland. Source

Kasabov N.,Auckland University of Technology
Neural Networks | Year: 2010

Spiking neural networks (SNN) are promising artificial neural network (ANN) models as they utilise information representation as trains of spikes, that adds new dimensions of time, frequency and phase to the structure and the functionality of ANN. The current SNN models though are deterministic, that restricts their applications for large scale engineering and cognitive modelling of stochastic processes. This paper proposes a novel probabilistic spiking neuron model (pSNM) and suggests ways of building pSNN for a wide range of applications including classification, string pattern recognition and associative memory. It also extends previously published computational neurogenetic models. © 2009 Elsevier Ltd. All rights reserved. Source

Kasabov N.K.,Auckland University of Technology
Neural Networks | Year: 2014

The brain functions as a spatio-temporal information processing machine. Spatio- and spectro-temporal brain data (STBD) are the most commonly collected data for measuring brain response to external stimuli. An enormous amount of such data has been already collected, including brain structural and functional data under different conditions, molecular and genetic data, in an attempt to make a progress in medicine, health, cognitive science, engineering, education, neuro-economics, Brain-Computer Interfaces (BCI), and games. Yet, there is no unifying computational framework to deal with all these types of data in order to better understand this data and the processes that generated it. Standard machine learning techniques only partially succeeded and they were not designed in the first instance to deal with such complex data. Therefore, there is a need for a new paradigm to deal with STBD. This paper reviews some methods of spiking neural networks (SNN) and argues that SNN are suitable for the creation of a unifying computational framework for learning and understanding of various STBD, such as EEG, fMRI, genetic, DTI, MEG, and NIRS, in their integration and interaction. One of the reasons is that SNN use the same computational principle that generates STBD, namely spiking information processing. This paper introduces a new SNN architecture, called NeuCube, for the creation of concrete models to map, learn and understand STBD. A NeuCube model is based on a 3D evolving SNN that is an approximate map of structural and functional areas of interest of the brain related to the modeling STBD. Gene information is included optionally in the form of gene regulatory networks (GRN) if this is relevant to the problem and the data. A NeuCube model learns from STBD and creates connections between clusters of neurons that manifest chains (trajectories) of neuronal activity. Once learning is applied, a NeuCube model can reproduce these trajectories, even if only part of the input STBD or the stimuli data is presented, thus acting as an associative memory. The NeuCube framework can be used not only to discover functional pathways from data, but also as a predictive system of brain activities, to predict and possibly, prevent certain events. Analysis of the internal structure of a model after training can reveal important spatio-temporal relationships 'hidden' in the data. NeuCube will allow the integration in one model of various brain data, information and knowledge, related to a single subject (personalized modeling) or to a population of subjects. The use of NeuCube for classification of STBD is illustrated in a case study problem of EEG data. NeuCube models result in a better accuracy of STBD classification than standard machine learning techniques. They are robust to noise (so typical in brain data) and facilitate a better interpretation of the results and understanding of the STBD and the brain conditions under which data was collected. Future directions for the use of SNN for STBD are discussed. © 2014 Elsevier Ltd. Source

Hankin R.K.S.,Auckland University of Technology
Journal of Statistical Software | Year: 2012

A multivariate generalization of the emulator technique described by Hankin (2005) is presented in which random multivariate functions may be assessed. In the standard univariate case (Oakley 1999), a Gaussian process, a finite number of observations is made; here, observations of different types are considered. The technique has the property that marginal analysis (that is, considering only a single observation type) reduces exactly to the univariate theory. The associated software is used to analyze datasets from the field of climate change. Source

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